Information dynamics of our brains in dynamically driven disordered superconducting loop networks
| Autor: | Goteti, Uday S. |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Complex systems of many interacting components exhibit patterns of recurrence and emergent behaviors in their time evolution that can be understood from a new perspective in physics of information dynamics modeled after one such system, our brains. A generic brain-like network model is derived from a system of disordered superconducting loops with Josephson junction oscillators to demonstrate these behaviors. The loops can trap multiples of fluxons that represent quantized information units in many distinct memory configurations populating a state space. The state can be updated by exciting the junctions to $fire$ or allow the movement of fluxons through the network as the current through them surpasses their thresholds. Simulations performed with a lumped circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on the 4-loop network shows dynamically stable flux flow in each pathway characterized by the junction firing statistics. The network separates information from multiple excitations into state categories with large energy barriers observed in simulations that correspond to different flux (information) flow patterns observed across junctions in experiments. Strong evidence for associative and time-dependent (short-to-long-term) memories distributed across the network is observed, dependent on its intrinsic and geometrical properties as described by the model. Suitable network topologies can model various other systems leading to a universal description of the nature of information dynamics. Comment: 6 figures, 26 pages |
| Databáze: | arXiv |
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